Mettle detection

Brush your teeth, wash your hands, eat your vegetables. Most of us are taught from an early age to make these simple steps part of our everyday lives.

And while an eight-year-old may need some persuasion, they’re all measures designed to pre-emptively stave off ill health of one sort or another.

With almost 7.5 million people awaiting NHS treatment, waiting lists continue to be as long as they have ever been. And with people living longer, healthcare systems around the world are struggling to cope with the demands of an ageing population.

Clinicians today are not only managing acute illnesses, they’re also contending with rising rates of chronic, long-term conditions like heart disease, diabetes, dementia and cancer. Determining what factors cause these conditions – whether genetic, environmental or lifestyle – is rarely straightforward.

It’s little wonder then that, just like a parent trying to get a child to munch down its greens, medical professionals prefer to prioritise prevention. Not only should this approach improve overall patient outcomes, it should also have the knock-on effect of alleviating the overall system burden.

It’s an approach that is far from simple but, with effective use of the latest technology, major advancements are well within reach.

Early detection is often perceived as a panacea. While identifying and treating a disease quickly does offer the best chance for effective intervention, it’s easier said than done.

Many chronic diseases develop silently, and by the time symptoms appear, the damage may already be done. Intervening at this point can be more complex and costly and is generally less effective.

Robust screening programmes and routine health checks are key to early detection, but they are resource intensive. They require investment in both infrastructure and workforce, and their success depends on consistent patient engagement – something that varies across different demographics and regions.

What’s more, these evidence-based practices are usually only targeted at a population-level – focusing on entire groups rather than individuals. As a result, the lack of personalisation means these measures aren’t as beneficial on a case-by-case basis.

When it comes to personalised care, technology can be an incredible tool – particularly for health systems under strain. AI and machine learning (ML) can target specific individuals to help identify which diseases a person may be predisposed to.

This approach allows for much more personalised treatment and advice, ensuring it’s tailored to an individual’s genetic makeup, lifestyle or environment.

Offering long-term savings in both time and resource, prevention is the ultimate goal. But the reality is, it’s never going to be 100% achievable.

Changes in human behaviour, demographics and resource will constantly be a barrier to prevention. But with the right tools and investment, the impact of preventative care could significantly improve health outcomes for patients.

When it comes to identifying what causes a chronic condition, the answer is rarely black and white.

Genetics certainly play a role, but lifestyle factors like diet, exercise, alcohol consumption and stress are also powerful influences. Untangling the interplay between these variables is one of healthcare’s enduring challenges.

Each patient is different. What triggers disease in one person might not in another.  For instance, the risk profile for conditions such as type 2 diabetes or cardiovascular disease can vary widely between individuals.

Two patients with the same genetic predisposition may experience vastly different outcomes depending on their diet, activity level, stress or exposure to environmental risks. Family history can point to potential risks but, without deeper insight, it’s often not enough to enable meaningful intervention.

On top of this, social and environmental determinants – such as access to green space, economic status and education – can dramatically influence health outcomes. Recognising risk, therefore, requires a holistic view that takes both biology and context into account.

Yet translating this insight into practical interventions remains difficult. The data exists, but connecting the dots at scale requires the right analytical tools. What’s more, how findings are communicated is a huge part of prevention.

Small lifestyle changes can make a huge impact on our overall health, but this isn’t always effectively conveyed to the wider population.

There is real promise in the role of technology when it comes to transforming how we prevent and treat chronic illness.

With advancements in data analysis, AI and ML we can now examine huge swathes of data across a large number of data sets from diverse populations. These technologies allow researchers to spot patterns that may not be caught by the human eye.

By comparing genetic data alongside patient histories, lifestyle information and social factors, we can begin to understand what truly drives disease onset.

In a recent study conducted by our parent business, Optima Partners, written in collaboration with Biogen and the University of Edinburgh, researchers made a significant breakthrough in predicting what diseases patients were at risk of.

The diseases spanned across Alzheimer’s, dementia, type 2 diabetes and heart diseases. By using cutting-edge ML, it was able to analyse vast amounts of medical data from the UK Biobank.

From there, it identified protein patterns, also known as protein signatures, that are linked to the risk of diseases. This allowed the researchers to accurately predict a person’s risk of disease up to ten years before diagnosis.

Such insights mean we can identify risk factors earlier and intervene more precisely.

If a patient is genetically predisposed to type 2 diabetes, clinicians could provide targeted lifestyle advice, schedule regular monitoring or even prescribe medication before symptoms appear.

Similarly, if a disease has already developed, it gives clinicians the opportunity to prevent other chronic illnesses – known as co-morbidities. For instance, diabetes can cause chronic kidney disease but, with regular exams and early intervention, clinicians could prevent this condition forming, even if the diabetes is unavoidable.

On a larger scale, health systems can use data to anticipate future demand and allocate resources accordingly. That could mean rolling out simpler, faster and more affordable screening techniques to improve diagnostics.

Take smear tests for example – they can be invasive, hard to access and not always accurate for cervical cancer. Innovation is clearly needed but the burden shouldn’t fall solely on the NHS – collaboration between clinicians and industry is essential.

Daye’s diagnostic tampons are a great example of using tech for non-invasive screening tools that can improve accuracy and patient convenience.

From the patients’ perspective, it gives them more peace of mind. Instead of receiving reactive care after something goes wrong, they benefit from personalised plans, earlier interventions and more control over their own health journey.

Crucially, this doesn’t replace clinicians, it supports them. By giving them richer insight, faster analysis and more precise diagnostic tools, technology acts as an enabler, not a substitute.

But for this approach to succeed, there needs to be investment: in technology infrastructure, in training and in trust. Done right, this kind of data-driven prevention could reshape healthcare – not just for the NHS, but globally.

As waiting lists grow and demands on healthcare systems increase, prevention becomes not just desirable – but essential. Yet it’s only through embracing technology, and the power of data, that we can truly deliver personalised, preventative care at scale.

By better understanding the complex mix of genetic and lifestyle factors behind chronic conditions, we can intervene earlier, improve patient outcomes and reduce the burden on already overstretched systems.

Prevention might be challenging. But with the right tools, it doesn’t have to be out of reach.

Dr Zhana Kuncheva is Director of Health Data Sciences at bioXcelerate